Comprehensive Economic Capital and Credit Risk Modelling Certification Training

This is an advanced professional program designed for risk management, credit risk, and analytics professionals seeking a deep, practical understanding of economic capital frameworks and credit risk modelling used in modern financial institutions. This course provides an integrated look at how credit risk models support key risk functions - from estimating risk parameters and validating models to linking them with regulatory capital, provisioning (CECL/IFRS 9), stress testing, and strategic decision-making.

Participants will explore foundational concepts such as probability of default (PD), loss given default (LGD), and exposure at default (EAD), and how these feed into economic capital and portfolio credit risk measurement under Basel III/IV. Comprehensive coverage of model development, validation standards, macroeconomic scenario design, advanced analytical methods (including machine learning), and practical case studies ensures that learners gain frameworks and tools that they can immediately apply in real-world risk management settings.

1. Basic understanding of finance, banking or risk management concepts
2. Familiarity with credit products, lending and balance-sheet principles
3. Comfort with numerical and analytical reasoning
4. No prior experience with CECL or economic capital models is required

1. Credit risk analysts and managers
2. Risk management professionals involved in ICAAP, CECL, IFRS 9 or stress testing
3. Finance professionals supporting credit portfolio management or capital planning
4. Internal audit and model validation teams focused on credit risk models
5. Senior management and decision-makers who rely on credit risk metrics for strategy

1. 20 multiple-choice questions covering all course modules
2. Passing requirement: at least 15 correct answers
3. Two attempts allowed to achieve a passing score
4. Certificate issued upon successful completion of the exam

1. Gain a strong understanding of economic capital frameworks and their role in risk management and strategic decision-making 
2.  Develop practical expertise in credit risk modelling techniques, including PD, LGD, and EAD estimation 
3.  Learn to quantify and manage risk in line with global regulatory standards such as Basel III
4.  Enhance your ability to perform stress testing and scenario analysis for effective risk forecasting 
5.  Understand the integration of economic capital with ICAAP and risk appetite frameworks
6.  Build hands-on skills in risk modelling, validation, and model governance
7.  Strengthen your knowledge of portfolio risk, capital allocation, and risk-adjusted performance measurement
8.  Improve your capability to support regulatory reporting and compliance requirements
9.  Gain practical insights through real-world case studies and industry applications
10.  Boost your career prospects in risk management, banking, and financial analytics roles

A FRM and PRM certified Financial Risk Management Consultant with international expertise in risk management methodological frameworks. He brings over 25 years of experience advising financial institutions and organisations on effective risk management practices. Qualified in Islamic Finance (IFQ), he is a published author on risk management and Basel Accords and a regular speaker at industry conferences. In addition, he delivers training courses and workshops for professionals across the globe, helping participants strengthen their risk management knowledge and skills.

1. Explain the role of credit risk modelling within enterprise risk management and its relationship with economic and regulatory capital frameworks.
2. Delineate key credit risk parameters - PD, LGD, and EAD - and understand how they inform provisioning, capital and risk measurement.
3. Describe the differences between economic capital and regulatory capital, including Basel III/IV implications and interactions with ICAAP and stress testing.
4. Develop and validate credit risk models using best practices for scoring, calibration, benchmarks and model risk governance.
5. Apply methods for portfolio-level credit risk modelling, including CreditMetrics, CreditRisk+ and KMV/EDF approaches.
6. Incorporate forward-looking macroeconomic variables in credit risk estimations and scenario designs for stress testing and capital planning.
7. Assess when qualitative adjustments and overlays are appropriate, including documentation and governance for supervisory scrutiny.
8. Evaluate advanced analytical techniques, including machine learning applications, while understanding regulatory expectations for explainability and bias control.
9. Integrate credit risk modelling outputs into enterprise risk processes, including stress testing, strategic planning and RAROC-based decision frameworks.